ArticleBMJ open diabetes research & care2022
Type 2 diabetes classification: a data-driven cluster study of the Danish Centre for Strategic Research in Type 2 Diabetes (DD2) cohort.
Article in BMJ open diabetes research & care, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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28 citing papers in PubMed, 46 citations in OpenAlex.
- Exploratory Analysis of Type 2 Diabetes Mellitus Metabolic Phenotypes and Their Association With Vitamin D Status in Primary Care Patients.Diabetes, obesity & metabolism · 2026Article
- Type 2 diabetes subtypes for precision medicine: methodological challenges and alternative prediction-based approaches.Diabetologia · 2026Review
- Data-Driven Multidimensional Clinical Phenotypes and Longitudinal Changes in Type 2 Diabetes Mellitus: A Retrospective Cohort Study.Biomedicines · 2026Article
- Phenotypic clustering of newly diagnosed type 2 diabetes in a Mediterranean cohort.Cardiovascular diabetology · 2026Article
- GLP-1 agonists and the gut microbiome: A bidirectional relationship.British journal of clinical pharmacology · 2026Review
- Genetic Susceptibility to Diabetes Subtypes and Risk of Developing Coronary Artery Disease.Diabetes care · 2026Article
- Pathophysiological Risk Factors Preceding Incidence of Type 2 Diabetes Subtypes: A Pooled Cohort Study in the United States.Diabetes care · 2026Article
- Diabetes mellitus as a multisystem disease: understanding subtypes, complications, and the link with steatotic liver diseases in humans.Hormones (Athens, Greece) · 2026Review
- Bridging the variant-to-function gap in type 2 diabetes: advances and challenges.Diabetologia · 2026Review
- Aetiological clustering of newly diagnosed type 2 diabetes using machine learning: a retrospective cross-sectional study in Dubai, UAE.BMJ open · 2025Article
- Appraisal of Clinical Explanatory Variables in Subtyping of Type 2 Diabetes Using Machine Learning Models.Journal of clinical medicine · 2025Article
- Prediction of metabolic subphenotypes of type 2 diabetes via continuous glucose monitoring and machine learning.Nature biomedical engineering · 2025Article
- Cluster Analysis in Diabetes Research: A Systematic Review Enhanced by a Cross-Sectional Study.Journal of clinical medicine · 2025Review
- Clinical application of cluster analysis in patients with newly diagnosed type 2 diabetes.Hormones (Athens, Greece) · 2025Article
- Subphenotypes of adult-onset diabetes: Data-driven clustering in the population-based KORA cohort.Diabetes, obesity & metabolism · 2025Article
- ICMR-MDRF Diabetes Biosamples: Cohort profile.The Indian journal of medical research · 2024Article
- Predicting Type 2 Diabetes Metabolic Phenotypes Using Continuous Glucose Monitoring and a Machine Learning Framework.medRxiv : the preprint server for health sciences · 2024Article
- Comparison of clustering and phenotyping approaches for subclassification of type 2 diabetes and its association with remission in Indian population.Scientific reports · 2024Article
- TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2024Article
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Authors and funding
19 authors at 7 institutions in 3 countries.
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No grant is acknowledged in the PubMed record.
Abstract
introductionA Swedish data-driven cluster study identified four distinct type 2 diabetes (T2D) clusters, based on age at diagnosis, body mass index (BMI), hemoglobin A1c (HbA1c) level, and homeostatic model assessment 2 (HOMA2) estimates of insulin resistance and beta-cell function. A Danish study proposed three T2D phenotypes (insulinopenic, hyperinsulinemic, and classical) based on HOMA2 measures only. We examined these two new T2D classifications using the Danish Centre for Strategic Research in Type 2 Diabetes cohort. RESEARCH DESIGN AND
methodsIn 3529 individuals, we first performed a k-means cluster analysis with a forced k-value of four to replicate the Swedish clusters: severe insulin deficient (SIDD), severe insulin resistant (SIRD), mild age-related (MARD), and mild obesity-related (MOD) diabetes. Next, we did an analysis open to alternative k-values (ie, data determined the optimal number of clusters). Finally, we compared the data-driven clusters with the three Danish phenotypes.
resultsCompared with the Swedish findings, the replicated Danish SIDD cluster included patients with lower mean HbA1c (86 mmol/mol vs 101 mmol/mol), and the Danish MOD cluster patients were less obese (mean BMI 32 kg/m
conclusionDifferent HOMA2-based approaches did not classify patients with T2D in a consistent manner. The T2D classes characterized by high insulin resistance/hyperinsulinemia appeared most distinct.
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